Diseases associated with age will reach pandemic proportions as the population both increases and ages simply because older humans are exposed to potential risk for longer periods of time. For instance, by 2020, age-associated cardiovascular disease alone will cause approximately 25 to 30 percent of all deaths in the world. Presently, treatment of age-associated diseases is predominately directed toward secondary management of observed clinical manifestations, risk factors, and associated adverse events. Risk factors are often considered consequences of underlying physiologic perturbations but these risk factors in and of themselves are not the primary cause of the disease manifestations ascribed to them. Risk factors are more likened to a consequence and not a cause.
Historically, patients have been assessed and managed on the basis of the presence or absence of these clinical risk factors and overt manifestations. By definition, risk factor management requires the patient to have a disease. The severity of the disease, moreover, may be indeterminate and an individual's response to therapy or the degree of preexisting disease burden is uncertain. This kind of risk management is not only user-unfriendly but in some instances is a disservice to the patient and the medical community. As an example, diabetes having a duration of one week most likely has a completely different risk burden than diabetes of 10 years' duration, so classification and treatment of these two disease states should be different and more importantly individualized.
Ideally, the choice of treatment of a disease should be based on the best evidence that comes from statistical research based on the measure of cause-related constituents. What we refer to as a disease is typically multivariable, comprises multiple and clustered risk factors, expresses variable or vacillating risk burdens, and demonstrates variable and/or indeterminate responses to therapy. A multivariable risk model is almost obligatory in order to assess a disease state because no single biomarker or feature is capable of measuring an individual's need for treatment or success in the prevention of adverse effects of the disease. The results of clinical trials are often used to predict the risk of disease with the ultimate goal leading to prevention of the disease in others. Clinical trials, however, typically follow a one-dimensional “top-down” approach with defined entry points, that is, the patient participating in the clinical trial has already passed a threshold which may be arbitrary and based on consensus. An “association” is frequently equated with cause-and-effect, however simply showing statistical independence or association is not adequate to demonstrate cause or clinical utility for risk prediction. The number of multivariable features of a disease interact with one another such that they not only change the interactions but, and this is more critical, the interaction may hide or erase their dependence on initial conditions. Another failure of clinical trials to evaluate risk factors to determine causation of a disease is that evaluation of multiple variables requires more complex analyses of all the factors necessitating a corresponding increase in the cohort size, often requiring hundreds or even thousands of patients. The averaging effects of these multitudes of patients in clinical trials, however, can give misleading results in the care of an actual individual having one or more of these multiple risk factors of variable duration and intensity. A “typical” patient does not fit the characteristics of an “average” patient so the clinical trials may actually contribute to over-treatment or under-treatment of the low- and high-risk patient groups.
Calculations of the long-term cost-effectiveness of treatment of risk factors are imprecise, and treatment recommendations are based principally on crude averages of disparate risk factors. Current management of risk factors certainly has benefits in terms of total number of years or quality-adjusted years gained, such as in the case of antihypertensive therapy. In general, however, the conventional approach to reporting overall results of clinical trials consigns the physician to an impoverished perspective in which risk data are flattened into a single effect: a therapy either works or doesn't. Treatment decisions are made easy because risk is fitted to the average patient and not real patients. No substantive clinical trials or cohort studies have defined risks, benefits, and costs of interventions on the basis of individual risk. As a result, existing guidelines for disease prevention have not achieved their objectives of controlling common risk factors.
Thus, the common practice of identification of complex multivariate clinical features and associated diseases might be interesting but does not address primary prevention of a disease. Primary prevention requires not only assessment of preclinical or emergent risk aggregates but also management of these risks before the disease expresses itself. Most diseases are consequences of an underlying physiological perturbations; thus risk assessment suited for primary prevention must have a different paradigm. Even though clinical trials recognize that disease states require analyses of many variables, conventional clinical risk algorithms have limited usefulness because the clinical risk factors studied may be poorly associated and do not have a numerical expression of disease intensity. Individual biomarkers or non-mathematical observations, moreover, may not be reproducible when attempting to predict emergent events. Indeed, management of a consequence does not ensure successful management of the cause. Without sufficiently addressing and quantifying both clinical and emergent risk burden of a disease state, treatment will be only partially successful for alleviating common diseases. Successful management of complex multivariate disease must transcend the limitations of the “one disease, one risk factor, and one seromarker” model and move medical science toward a more comprehensive and clinically realistic scenario.
Predictive modeling is one technique used to predict disease. In general, predictive modeling algorithms incorporate mathematical algorithms that interpret historical data and make predictions about the future. Predictive modeling, however, also has shortcomings especially when applied to prediction of disease. As mentioned above, the clinical models used to collect data involve people already having the disease and not the emergent risk embedded in the general public. Statistically speaking, the use of subjects having the disease is already a skewed population resulting in a collection of data points at an extreme side of the distribution, i.e., to the far left or the far right of the normal bell curve. It is known in the medical literature that multivariable risk models based on disparate observed risk factors and complex modifiers are difficult to assess. Further, it is a fact that individual risk and management predicated on clinical risk modifications or event incidences do not prevent the occurrence of the observed factors.
To further hinder the application of predictive modeling to disease states, most doctors are unaware of relevant results from evidence-based medicine studies, are overwhelmed by the diversity and magnitude of the medical literature or both. Advances in internet databases and information retrieval technology have spurred a new technology of analysis and dissemination of medical information to the decision makers. Telemedicine and super-crunchers, the current internet aids, and focus on diagnostic decision-support software are prompted by input of clinical findings. It is presumed that an internet search of the information embedded in the aggregate health care experience will enable a physician to make more informed diagnoses, decrease misdiagnosis and enhance the application of evidence-based medicine. These internet diagnostic software tools typically use a taxonomy of diseases to statistically search journal articles or working groups for word patterns most likely to be associated with the various diseases. Despite the best efforts and hopes, super information crunchers are currently applied in a top-down search for diagnoses and have been successful only about ten percent of the time. The paradigm is flawed from the beginning; merely finding data of a clinically apparent disease doesn't inform a patient or a doctor how to prevent the disease.
To move into a different paradigm of disease prevention and in the context of the embodiments described herein, it becomes useful to discuss the differences between data, metadata, understanding, and knowledge. Data are numbers derived from observation, mathematical calculation or experiments, and are typically acquired using a machine. Information is data in context; information is a collection of data and associated explanations, interpretations or discussions concerning a particular object, event or process, e.g., a diagnostician's interpretation of data's relationship to normal or abnormal states. Metadata is data about data and describes the context in which the information was obtained or is used, e.g., summaries and high-level interpretation of data such as a “final report”. Understanding is the use of metadata and information to make logical choices, e.g., a doctor selects features or tests when considering a particular disease and/or patient. Understanding is also considered the human capacity to render experience intelligible by relating specific knowledge to broad concepts. Knowledge is a combination of metadata and an awareness of the context in which metadata can be successfully applied, e.g., the relationships between features. In artificial intelligence, knowledge determines how to use and relate information and metadata. Accumulated knowledge when applied to artificial intelligence algorithms is commonly referred to as a knowledgebase 140. In general, a knowledgebase 140 is a centralized repository of information and knowledge. Each knowledgebase 140 is unique to the expert or experts from which it emanates but an undisciplined knowledgebase 140 is incapable of yielding high order prediction. Clinical medicine has explored use of diverse forms of information science for determination of wellness and management of disease but so far implementation of these technologies has not successfully replicated or replaced the complex multivariable knowledgebase 140 of the medical providers having associative knowledge of the disease constituents, e.g., physicians, specialists and technologists. So far, the use of artificial intelligence per se in clinical medicine remains illusive and unattainable.
Informatics includes the general science of information, the practice of information processing and engineering of information systems. Informatics is the study of the structure, behavior and interaction of natural and artificial systems that store, process and communicate information. Health and medical informatics deals with the resources, devices and methods required to optimize the acquisition, storage, retrieval and use of information in health and biomedicine. On the other hand, information science, of which complexity science is included, is an interdisciplinary science of the collection, classification, manipulation, reporting, storage, retrieval and dissemination of information. Information science and informatics are thus very similar, with information science generally being considered a branch of computer science and informatics is a more closely related to the cognitive and social sciences.
Complexity science is an emerging study wherein scientists often seek simple non-linear coupling rules that result in complex phenomena. Human societies, human brains are examples of complex systems in which neither the components nor the couplings are simple or linear. Nevertheless, they exhibit many of the hallmarks of complex systems. Although biological systems are typically nonlinear, non-linearity is not a necessary feature of complex systems modeling: useful macro-analyses of unstable equilibrium and evolution processes of certain biological/social/economic systems can be carried out also by sets of linear equations, which do nevertheless entail reciprocal associative dependence between variable parameters. Of particular import here, disease can be studied as a complex system. In complexity science numerical expressions of natural laws are called features. A feature is considered a characteristic if it permits recognition of an event. For instance, one person recognizes another person by such features as sex, skin, eyes, height, etc. In complexity science, these features are assembled into small sets, called feature-sets, of highly associated features that reinforce prediction. Each successive encounter of the “stranger” reinforces the small feature-set. Disparate features and less connected features such as skin temperature and clothing are not particularly helpful in assuring repeated recognition.
Confident prediction of subclinical or pre-emergent disease is essential to prediction and prevention of disease and management of the current medical crisis but current disease prediction and management are insufficient. There are numerous sources of relevant medical data derived from various state-of-the-art medical technologies where data are typically expressed as numerical variables related to normal or abnormal states. Application of data informatics and information science which are intended to assist in predicting or directing medical care have met limited clinical utility in the management of human disease. Such information solutions include: telemedicine, clinical trials, clinical risk scores, binary gaming algorithms, super-crunchers, etc. True artificial intelligence remains and will remain impractical for a few more decades. However, in the context of information science, complexity science is a powerful predictor of disease and determiner of the magnitude of that risk, as described herein. Complexity science has been used in medicine in a comparison of prediction accuracy, complexity and training time of classification algorithms. There are published articles on the application of nonlinear and linear dynamics: chaos theory, fractals and complexity for physicians at the bedside. Complexity science has been principally applied to poorly-connected disparate features of a clinical setting. Complexity science has also been more commonly applied to the social sciences.
The medical community has yet to identify and embrace a feature-set of risk models, also called disease surrogates, which are capable of detecting disease in its formative or pre-emergent stages. Identification and individual characterization of asymptomatic subjects in the general population who carry a high risk remains problematic and inadequate. To date, no satisfactory solution to this dilemma has been adopted.